Problem Statement

For this particular assignment, the data of different types of wine sales in the 20th century is to be analysed. Both of these data are from the same company but of different wines. As an analyst in the ABC Estate Wines, you are tasked to analyse and forecast Wine Sales in the 20th century.

---

Importing necessary libraries

from statsmodels.tsa.arima.model import ARIMA as ar
from statsmodels.tsa.seasonal import seasonal_decompose
from sklearn.linear_model import LinearRegression
import statsmodels as st
from statsmodels.tsa.api import ExponentialSmoothing, SimpleExpSmoothing, Holt
from statsmodels.tsa.arima.model import ARIMA
import itertools
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf


import numpy as np
import pandas as pd
from sklearn import metrics
import matplotlib.pyplot as plt
#import plotly.offline as py

%matplotlib inline
import seaborn as sns
from pylab import rcParams

Loading the dataset

df = pd.read_csv("D:/Deakin university master degree/time series forcasting/project/Sparkling.csv") ##  Fill the blank to read the data

Data Overview

Displaying the first few rows of the dataset

df.head() ##  Complete the code to view top 5 rows of the data

	YearMonth	Sparkling
0	1980-01	1686
1	1980-02	1591
2	1980-03	2304
3	1980-04	1712
4	1980-05	1471

Checking the shape of the dataset

# checking shape of the data
print(f"There are {df.shape[0]} rows and {df.shape[1]} columns.") # Complete the code to view dimensions of the data

There are 187 rows and 2 columns.

Checking the data types of the columns for the dataset

# checking column datatypes and number of non-null values
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 187 entries, 0 to 186
Data columns (total 2 columns):
 #   Column     Non-Null Count  Dtype 
---  ------     --------------  ----- 
 0   YearMonth  187 non-null    object
 1   Sparkling  187 non-null    int64 
dtypes: int64(1), object(1)
memory usage: 3.1+ KB

Observations

• YearMonth is object type columns and Sparkling is numerical type column.

Data Pre processing

Time_Stamp = pd.date_range(start='1980-01-01',periods=len(df),freq='M')
Time_Stamp

DatetimeIndex(['1980-01-31', '1980-02-29', '1980-03-31', '1980-04-30',
               '1980-05-31', '1980-06-30', '1980-07-31', '1980-08-31',
               '1980-09-30', '1980-10-31',
               ...
               '1994-10-31', '1994-11-30', '1994-12-31', '1995-01-31',
               '1995-02-28', '1995-03-31', '1995-04-30', '1995-05-31',
               '1995-06-30', '1995-07-31'],
              dtype='datetime64[ns]', length=187, freq='M')

df['Time_Stamp'] = Time_Stamp
df.head()

	YearMonth	Sparkling	Time_Stamp
0	1980-01	1686	1980-01-31
1	1980-02	1591	1980-02-29
2	1980-03	2304	1980-03-31
3	1980-04	1712	1980-04-30
4	1980-05	1471	1980-05-31

df.set_index(keys='Time_Stamp',inplace=True)
df

	YearMonth	Sparkling
Time_Stamp
1980-01-31	1980-01	1686
1980-02-29	1980-02	1591
1980-03-31	1980-03	2304
1980-04-30	1980-04	1712
1980-05-31	1980-05	1471
...	...	...
1995-03-31	1995-03	1897
1995-04-30	1995-04	1862
1995-05-31	1995-05	1670
1995-06-30	1995-06	1688
1995-07-31	1995-07	2031

187 rows × 2 columns

df.drop(['YearMonth'],axis=1,inplace=True) #Complete the code to drop the column 'YearMonth'

print(df.shape)

(187, 1)

Checking for missing values

Before we start exploring the data further, let's quickly check the missingness in the data.

df.isnull().sum() # Complete the code to check the presence of missing values 

Sparkling    0
dtype: int64

df.head() ##  Complete the code to view top 5 rows of the data

	Sparkling
Time_Stamp
1980-01-31	1686
1980-02-29	1591
1980-03-31	2304
1980-04-30	1712
1980-05-31	1471

Exploratory Data Analysis (EDA)

Bivariate Analysis

Timestamp vs Sparkling

## let's plot the sparkling vs timestamp 
plt.figure(figsize=(22, 8))
plt.plot(df);
plt.grid()

• it seem the data has seasonality with no trend

plt.figure(figsize=(22, 8))
sns.boxplot(x = df.index.year,y = df['Sparkling']) # Complete the code to check the relationship between the 'Sparkling' column and 'Time_Stamp' 
plt.grid(); 

• The median value fluctuates slightly from year to year, with noticeable differences in the height of the boxes.
• The variability (height of the boxes and whiskers) increases in some years indicating a wider range of "Sparkling" values.

plt.figure(figsize=(22, 8))
sns.boxplot(x = df.index.month_name(),y = df['Sparkling']) # Complete the code to check the relationship between the 'Sparkling' column and 'Time_Stamp' 
plt.grid();

October, November , December are high seanons

Decomposition

decomposition = seasonal_decompose(df,model='additive') ##Complete the code to check additive Decomposition
decomposition.plot(); 

• We see that most of the residuals are located around 0 from the plot of the residuals in the decomposition.
• From 1983 until 1989 trend increased and slightly decreased from 1989 to 1994 in the trend decomposition

trend = decomposition.trend
seasonality = decomposition.seasonal
residual = decomposition.resid

print('Trend','\n',trend.head(12),'\n')
print('Seasonality','\n',seasonality.head(12),'\n')
print('Residual','\n',residual.head(12),'\n')

Trend 
 Time_Stamp
1980-01-31            NaN
1980-02-29            NaN
1980-03-31            NaN
1980-04-30            NaN
1980-05-31            NaN
1980-06-30            NaN
1980-07-31    2360.666667
1980-08-31    2351.333333
1980-09-30    2320.541667
1980-10-31    2303.583333
1980-11-30    2302.041667
1980-12-31    2293.791667
Name: trend, dtype: float64 

Seasonality 
 Time_Stamp
1980-01-31    -854.260599
1980-02-29    -830.350678
1980-03-31    -592.356630
1980-04-30    -658.490559
1980-05-31    -824.416154
1980-06-30    -967.434011
1980-07-31    -465.502265
1980-08-31    -214.332821
1980-09-30    -254.677265
1980-10-31     599.769957
1980-11-30    1675.067179
1980-12-31    3386.983846
Name: seasonal, dtype: float64 

Residual 
 Time_Stamp
1980-01-31           NaN
1980-02-29           NaN
1980-03-31           NaN
1980-04-30           NaN
1980-05-31           NaN
1980-06-30           NaN
1980-07-31     70.835599
1980-08-31    315.999487
1980-09-30    -81.864401
1980-10-31   -307.353290
1980-11-30    109.891154
1980-12-31   -501.775513
Name: resid, dtype: float64 

• Trend:Indicates a slight downward trend in the data during 1980.
• Seasonality:Shows regular, repeating fluctuations, such as a peak in December (3386.98) and a dip in months like January (-854.26) and June (-967.43).
• Residual:Represents the randomness or noise in the data, which might be influenced by unique, non-repeating factors.

Checking for additive assumptions

residual.mean()

-1.2088458994707376

• Mean of risual -1.21 is very close to zero and it is a good sign that the additive decomposition worked well, but it may indicate a slight bias.

##Normality Distribution of Resid
from scipy.stats import shapiro

sns.distplot(residual)

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\2194742101.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(residual)

<Axes: xlabel='resid', ylabel='Density'>

• The distribution of Residuals are almost normal distribution

shapiro(residual.dropna())

ShapiroResult(statistic=0.9833053350448608, pvalue=0.0343334935605526)

• While your data is close to normal, the Shapiro-Wilk test indicates a significant deviation from normality.

Multiplicative Decomposition

#Multiplicative Decomposition
decomposition = seasonal_decompose(df,model='multiplicative') # complete the code to multiplicative decomposition
decomposition.plot();

• We see that most of the residuals are located around 0 from the plot of the residuals in the decomposition.
• From 1983 until 1989 trend increased and slightly decreased from 1989 to 1994 in the trend decomposition

trend = decomposition.trend
seasonality = decomposition.seasonal
residual = decomposition.resid

print('Trend','\n',trend.head(12),'\n')
print('Seasonality','\n',seasonality.head(12),'\n')
print('Residual','\n',residual.head(12),'\n')

Trend 
 Time_Stamp
1980-01-31            NaN
1980-02-29            NaN
1980-03-31            NaN
1980-04-30            NaN
1980-05-31            NaN
1980-06-30            NaN
1980-07-31    2360.666667
1980-08-31    2351.333333
1980-09-30    2320.541667
1980-10-31    2303.583333
1980-11-30    2302.041667
1980-12-31    2293.791667
Name: trend, dtype: float64 

Seasonality 
 Time_Stamp
1980-01-31    0.649843
1980-02-29    0.659214
1980-03-31    0.757440
1980-04-30    0.730351
1980-05-31    0.660609
1980-06-30    0.603468
1980-07-31    0.809164
1980-08-31    0.918822
1980-09-30    0.894367
1980-10-31    1.241789
1980-11-30    1.690158
1980-12-31    2.384776
Name: seasonal, dtype: float64 

Residual 
 Time_Stamp
1980-01-31         NaN
1980-02-29         NaN
1980-03-31         NaN
1980-04-30         NaN
1980-05-31         NaN
1980-06-30         NaN
1980-07-31    1.029230
1980-08-31    1.135407
1980-09-30    0.955954
1980-10-31    0.907513
1980-11-30    1.050423
1980-12-31    0.946770
Name: resid, dtype: float64 

• Trend:There’s a slight downward trend in the data in 1980, as shown by the gradually decreasing values in the trend component. This suggests a general decline over time, excluding seasonality.
• Seasonality: The seasonal component exhibits periodic fluctuations, with some months (such as December) showing much stronger seasonal effects (2.38) compared to others like January (0.65).
• Residual:These values are relatively small and consistent, suggesting that the decomposition model is reasonably good at capturing the trend and seasonality, leaving only small, random deviations.

Checking for Multiplicative assumptions

residual = decomposition.resid
residual.mean()

0.9997456359115032

• The residual mean of 0.9997 indicates that the multiplicative decomposition is performing well. The model appears to have effectively captured the underlying trend and seasonal patterns in the data, leaving only small, random residuals.

sns.distplot(residual)

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1108040850.py:1: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(residual)

<Axes: xlabel='resid', ylabel='Density'>

• The distribution of Residuals are almost normal distribution

shapiro(residual.dropna())

ShapiroResult(statistic=0.9859988689422607, pvalue=0.07802142202854156)

• The Shapiro-Wilk test suggests that the data is approximately normally distributed,
• p-value (0.078) is greater than the significance level of 0.05, you fail to reject the null hypothesis. This means there is not enough evidence to conclude that the data is not normally distributed.

df.describe()

	Sparkling
count	187.000000
mean	2402.417112
std	1295.111540
min	1070.000000
25%	1605.000000
50%	1874.000000
75%	2549.000000
max	7242.000000

• The minimum value of 1070 and the maximum value of 7242 show a wide range of values.
• The data appears to have a moderate spread as indicated by the standard deviation (1295.11) being a significant proportion of the mean (2402.42).

Data Preparation for Modeling

df.index.year.unique()  ## Complete the code to check the unique values

Int64Index([1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990,
            1991, 1992, 1993, 1994, 1995],
           dtype='int64', name='Time_Stamp')

### Complete the code to take all data till the year 1991 in the train set and everything after that in the test set
df_train = df[df.index.year <= 1991] 
df_test = df[df.index.year > 1991]

print(df_train.shape)
print(df_test.shape)

(144, 1)
(43, 1)

Let's check the train dataset

df_train.head()

	Sparkling
Time_Stamp
1980-01-31	1686
1980-02-29	1591
1980-03-31	2304
1980-04-30	1712
1980-05-31	1471

Let's check the test dataset

df_test.head()

	Sparkling
Time_Stamp
1992-01-31	1577
1992-02-29	1667
1992-03-31	1993
1992-04-30	1997
1992-05-31	1783

Model Building

Linear Regression Model

• For this particular linear regression, we are going to regress the 'Sparkling' variable against the order of the occurrence.
• For this we need to modify our training data before fitting it into a linear regression.

train_time = [i+1 for i in range(len(df_train))]
test_time = [i+133 for i in range(len(df_test))]
print('Training Time instance','\n',train_time)
print('Test Time instance','\n',test_time)

Training Time instance 
 [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144]
Test Time instance 
 [133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175]

LinearRegression_train = df_train.copy()
LinearRegression_test = df_test.copy()

LinearRegression_train['time'] = train_time
LinearRegression_test['time'] = test_time

print('First few rows of Training Data','\n',LinearRegression_train.head(),'\n')
print('Last few rows of Training Data','\n',LinearRegression_train.tail(),'\n')
print('First few rows of Test Data','\n',LinearRegression_test.head(),'\n')
print('Last few rows of Test Data','\n',LinearRegression_test.tail(),'\n')

First few rows of Training Data 
             Sparkling  time
Time_Stamp                 
1980-01-31       1686     1
1980-02-29       1591     2
1980-03-31       2304     3
1980-04-30       1712     4
1980-05-31       1471     5 

Last few rows of Training Data 
             Sparkling  time
Time_Stamp                 
1991-08-31       1857   140
1991-09-30       2408   141
1991-10-31       3252   142
1991-11-30       3627   143
1991-12-31       6153   144 

First few rows of Test Data 
             Sparkling  time
Time_Stamp                 
1992-01-31       1577   133
1992-02-29       1667   134
1992-03-31       1993   135
1992-04-30       1997   136
1992-05-31       1783   137 

Last few rows of Test Data 
             Sparkling  time
Time_Stamp                 
1995-03-31       1897   171
1995-04-30       1862   172
1995-05-31       1670   173
1995-06-30       1688   174
1995-07-31       2031   175 

lr = LinearRegression()

LinearRegression_train['Sparkling'].values

array([1686, 1591, 2304, 1712, 1471, 1377, 1966, 2453, 1984, 2596, 4087,
       5179, 1530, 1523, 1633, 1976, 1170, 1480, 1781, 2472, 1981, 2273,
       3857, 4551, 1510, 1329, 1518, 1790, 1537, 1449, 1954, 1897, 1706,
       2514, 3593, 4524, 1609, 1638, 2030, 1375, 1320, 1245, 1600, 2298,
       2191, 2511, 3440, 4923, 1609, 1435, 2061, 1789, 1567, 1404, 1597,
       3159, 1759, 2504, 4273, 5274, 1771, 1682, 1846, 1589, 1896, 1379,
       1645, 2512, 1771, 3727, 4388, 5434, 1606, 1523, 1577, 1605, 1765,
       1403, 2584, 3318, 1562, 2349, 3987, 5891, 1389, 1442, 1548, 1935,
       1518, 1250, 1847, 1930, 2638, 3114, 4405, 7242, 1853, 1779, 2108,
       2336, 1728, 1661, 2230, 1645, 2421, 3740, 4988, 6757, 1757, 1394,
       1982, 1650, 1654, 1406, 1971, 1968, 2608, 3845, 4514, 6694, 1720,
       1321, 1859, 1628, 1615, 1457, 1899, 1605, 2424, 3116, 4286, 6047,
       1902, 2049, 1874, 1279, 1432, 1540, 2214, 1857, 2408, 3252, 3627,
       6153], dtype=int64)

lr.fit(LinearRegression_train[['time']],LinearRegression_train['Sparkling'].values)

LinearRegression()

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

test_prediction_model = lr.predict(LinearRegression_test[['time']])
LinearRegression_test['RegOnTime']=test_prediction_model

plt.figure(figsize=(15,12))
plt.plot(df_train['Sparkling'],label='Train')
plt.plot(df_test['Sparkling'],label='Test')
plt.plot(LinearRegression_test['RegOnTime'],label='Regression On Time_Test Data')
plt.legend(loc='best')
plt.grid();

rmse_model_test= metrics.mean_squared_error(df_test['Sparkling'],test_prediction_model,squared=False)
print("For RegressionOnTime forecast on the Test Data,  RMSE is %3.3f" %(rmse_model_test))

For RegressionOnTime forecast on the Test Data,  RMSE is 1337.090

resultsDf = pd.DataFrame({'Test RMSE': [rmse_model_test]},index=['RegressionOnTime'])
resultsDf

	Test RMSE
RegressionOnTime	1337.09022

• The RMSE value (1337.090) seems fairly large relative to the mean of 2402.42 and the spread of the data. The model needs improvement

Naive Approach Model

For this particular naive model, we say that the prediction for tomorrow is the same as today and the prediction for day after tomorrow is tomorrow and since the prediction of tomorrow is same as today,therefore the prediction for day after tomorrow is also today.

NaiveModel_train = df_train.copy() # Complete the code to create a copy of the train datasets
NaiveModel_test = df_test.copy() # Complete the code to create a copy of the test datasets

NaiveModel_train.shape

(144, 1)

NaiveModel_test.shape

(43, 1)

NaiveModel_test.head()

	Sparkling
Time_Stamp
1992-01-31	1577
1992-02-29	1667
1992-03-31	1993
1992-04-30	1997
1992-05-31	1783

NaiveModel_test['naive'] = np.asarray(df_train['Sparkling'])[len(np.asarray(df_train['Sparkling']))-1]
NaiveModel_test['naive'].head()

Time_Stamp
1992-01-31    6153
1992-02-29    6153
1992-03-31    6153
1992-04-30    6153
1992-05-31    6153
Name: naive, dtype: int64

NaiveModel_test.head()

	Sparkling	naive
Time_Stamp
1992-01-31	1577	6153
1992-02-29	1667	6153
1992-03-31	1993	6153
1992-04-30	1997	6153
1992-05-31	1783	6153

plt.figure(figsize=(15,12))
plt.plot(NaiveModel_train['Sparkling'],label='Train')
plt.plot(df_test['Sparkling'],label='Test')
plt.plot(NaiveModel_test['naive'], label='Naive Forecast on test data')
plt.legend(loc='best')
plt.title('Naive Forecast')
plt.grid();

##MODEL EVAULATION
rmse_model_test = metrics.mean_squared_error(df_test['Sparkling'],NaiveModel_test['naive'],squared=False)
print("For Naive On Time Forecast on the Test Data, RMSE is %3.3f" %(rmse_model_test))

For Naive On Time Forecast on the Test Data, RMSE is 3979.915

• RMSE of 3979.915 is quite high in relation to the data's mean (2402.42) and variability (std: 1295.11). This indicates that the Naive Forecast is unsuitable for your dataset.

resultsDfN = pd.DataFrame({'Test RMSE': [rmse_model_test]}, index=['NaiveModel']) # Complete the code to check the perfromance of the 'NaiveModel' 
resultsDf = pd.concat([resultsDf,resultsDfN])
resultsDf

	Test RMSE
RegressionOnTime	1337.090220
NaiveModel	3979.914692

Simple Average Model

For this particular simple average method, we will forecast by using the average of the training values.

# Let's create the copy of the train and test dataset 
SimpleAverage_train = df_train.copy() # Complete the code to create a copy of the train datasets
SimpleAverage_test = df_test.copy() # Complete the code to create a copy of the test datasets

SimpleAverage_test['mean forecast']= df_train['Sparkling'].mean()
SimpleAverage_test.head()

	Sparkling	mean forecast
Time_Stamp
1992-01-31	1577	2408.930556
1992-02-29	1667	2408.930556
1992-03-31	1993	2408.930556
1992-04-30	1997	2408.930556
1992-05-31	1783	2408.930556

plt.figure(figsize=(15,12))
plt.plot(df_train['Sparkling'],label='Train')
plt.plot(df_test['Sparkling'], label='Test')
plt.plot(SimpleAverage_test['mean forecast'],label='Simple Average on Test Data')
plt.legend(loc='best')
plt.title("Simple Average Forecast")
plt.grid();

##MODEL EVAULATION
rmse_model_test = metrics.mean_squared_error(df_test['Sparkling'],SimpleAverage_test['mean forecast'],squared=False)
print("For Simple Average Forecast on Test Data, RMSE is %3.3f"%(rmse_model_test))

For Simple Average Forecast on Test Data, RMSE is 1268.683

• Compared to the Naive Model's RMSE of 3979.915, this is a significant improvement, indicating that the Simple Average Forecast is a better baseline model for your dataset.
• However, the RMSE being close to the standard deviation suggests that the model does not capture the variability in the data effectively.

resultsDfSES = pd.DataFrame({'Test RMSE':  [rmse_model_test]},index=['SimpleAverageModel']) # Complete the code to check the perfromance of the 'SimpleAverageModel' 
resultsDf = pd.concat([resultsDf,resultsDfSES])

resultsDf

	Test RMSE
RegressionOnTime	1337.090220
NaiveModel	3979.914692
SimpleAverageModel	1268.683035

Simple Exponential Smoothing Model

# Let's create the train and test dataset 
SES_train = df_train.copy() # Complete the code to create a copy of the train datasets
SES_test =  df_test.copy() # Complete the code to create a copy of the test datasets

model_SES = SimpleExpSmoothing(SES_train['Sparkling'])

C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)

model_SES_autofit = model_SES.fit(optimized=True)

model_SES_autofit.params

{'smoothing_level': 0.03953488372093023,
 'smoothing_trend': nan,
 'smoothing_seasonal': nan,
 'damping_trend': nan,
 'initial_level': 1686.0,
 'initial_trend': nan,
 'initial_seasons': array([], dtype=float64),
 'use_boxcox': False,
 'lamda': None,
 'remove_bias': False}

SES_test['predict'] = model_SES_autofit.forecast(steps=len(df_test))

SES_test.head()

	Sparkling	predict
Time_Stamp
1992-01-31	1577	2648.540985
1992-02-29	1667	2648.540985
1992-03-31	1993	2648.540985
1992-04-30	1997	2648.540985
1992-05-31	1783	2648.540985

## Plotting on both the Training and Test data

plt.figure(figsize=(16,8))
plt.plot(SES_train['Sparkling'], label='Train')
plt.plot(SES_test['Sparkling'], label='Test')

plt.plot(SES_test['predict'], label='Alpha = 0.05 Simple Exponential Smoothing predictions on Test Set')

plt.legend(loc='best')
plt.grid()
plt.title('Alpha = 0.05 Predictions');

Model Evaluation for 𝛼 = 0.05 : Simple Exponential Smoothing

rmse_model_test = metrics.mean_squared_error(SES_test['Sparkling'],SES_test['predict'],squared=False)
print("For Alpha = 0.05 SES Model on Test Data,RMSE is %3.3f" %(rmse_model_test))

For Alpha = 0.05 SES Model on Test Data,RMSE is 1296.358

resultsDf_1 = pd.DataFrame({'Test RMSE': [rmse_model_test]},index=['SimpleExponentialSmoothing'])   # Complete the code to check the perfromance of the 'Alpha = 0.05,SimpleExponentialSmoothing' 
resultsDf = pd.concat([resultsDf, resultsDf_1])
resultsDf

	Test RMSE
RegressionOnTime	1337.090220
NaiveModel	3979.914692
SimpleAverageModel	1268.683035
SimpleExponentialSmoothing	1296.358039

• This result suggests that the SES model with 𝛼=0.05, performs slightly worse than the Simple Average Forecast (RMSE= 1268.683) but significantly better than the Naive Forecast (RMSE = 3979.915).

• The RMSE of 1296.358 suggests that the SES model with 𝛼 = 0.05 performs moderately well, capturing the central tendency of the data but failing to adjust effectively to recent changes due to the low 𝛼.

Setting different alpha values.

Remember, the higher the alpha value more weightage is given to the more recent observation. That means, what happened recently will happen again. We will run a loop with different alpha values to understand which particular value works best for alpha on the test set.

First we will define an empty dataframe to store our values from the loop

resultsDf_a = pd.DataFrame({'Alpha Values':[],'Train RMSE':[],'Test RMSE': []})
resultsDf_a

	Alpha Values	Train RMSE	Test RMSE

for i in np.arange(0.3,1,0.1):
    model_SES_alpha_i = model_SES.fit(smoothing_level=i,optimized=False,use_brute=True)
    SES_train['predict',i] = model_SES_alpha_i.fittedvalues
    SES_test['predict',i] = model_SES_alpha_i.forecast(steps=55)
    
    rmse_model5_train_i = metrics.mean_squared_error(SES_train['Sparkling'],SES_train['predict',i],squared=False)
    
    rmse_model5_test_i = metrics.mean_squared_error(SES_test['Sparkling'],SES_test['predict',i],squared=False)
    
    resultsDf_a = resultsDf_a.append({'Alpha Values':i,'Train RMSE':rmse_model5_train_i 
                                      ,'Test RMSE':rmse_model5_test_i}, ignore_index=True)

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3058314993.py:10: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_a = resultsDf_a.append({'Alpha Values':i,'Train RMSE':rmse_model5_train_i
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3058314993.py:10: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_a = resultsDf_a.append({'Alpha Values':i,'Train RMSE':rmse_model5_train_i
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3058314993.py:10: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_a = resultsDf_a.append({'Alpha Values':i,'Train RMSE':rmse_model5_train_i
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3058314993.py:10: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_a = resultsDf_a.append({'Alpha Values':i,'Train RMSE':rmse_model5_train_i
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3058314993.py:10: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_a = resultsDf_a.append({'Alpha Values':i,'Train RMSE':rmse_model5_train_i
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3058314993.py:10: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_a = resultsDf_a.append({'Alpha Values':i,'Train RMSE':rmse_model5_train_i
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3058314993.py:10: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_a = resultsDf_a.append({'Alpha Values':i,'Train RMSE':rmse_model5_train_i

resultsDf_a.sort_values(by=['Test RMSE'],ascending=True)

	Alpha Values	Train RMSE	Test RMSE
0	0.3	1362.731346	1900.058569
1	0.4	1356.208919	2260.069389
2	0.5	1347.944758	2606.296390
3	0.6	1343.099607	2924.118301
4	0.7	1343.640190	3214.744366
5	0.8	1349.991473	3483.731806
6	0.9	1362.270034	3736.981096

• The Test RMSE increases consistently as α increases.
• The lowest Test RMSE is achieved at α=0.3 (1900.06), which indicates the best generalization to unseen data.

## Plotting on both the Training and Test data

plt.figure(figsize=(18,9))
plt.plot(SES_train['Sparkling'], label='Train')
plt.plot(SES_test['Sparkling'], label='Test')

plt.plot(SES_test['predict'], label='Alpha = 0.05 Simple Exponential Smoothing predictions on Test Set')

plt.plot(SES_test['predict', 0.3], label='Alpha = 0.3 Simple Exponential Smoothing predictions on Test Set')



plt.legend(loc='best')
plt.grid();

resultsDf_2 = pd.DataFrame({'Test RMSE': [resultsDf_a.sort_values(by=['Test RMSE'],ascending=True).values[0][2]]}
                           ,index=['SimpleExponentialSmoothing(α=0.3)'])  # Complete the code to check the perfromance of the 'Alpha=0.3,SimpleExponentialSmoothing' 

resultsDf = pd.concat([resultsDf, resultsDf_2])
resultsDf

	Test RMSE
RegressionOnTime	1337.090220
NaiveModel	3979.914692
SimpleAverageModel	1268.683035
SimpleExponentialSmoothing	1296.358039
SimpleExponentialSmoothing(α=0.3)	1900.058569

Double Exponential Smoothing (Holt's Model)

Two parameters 𝛼 and 𝛽 are estimated in this model. Level and Trend are accounted for in this model.

DES_train = df_train.copy() # Complete the code to create a copy of the train dataset
DES_test =df_test.copy() # Complete the code to create a copy of the test dataset

model_DES =  Holt(DES_train['Sparkling'])

C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)

model_DES_autofit = model_DES.fit()

C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\tsa\holtwinters\model.py:917: ConvergenceWarning: Optimization failed to converge. Check mle_retvals.
  warnings.warn(

model_DES_autofit.params

{'smoothing_level': 0.6885714285714285,
 'smoothing_trend': 9.999999999999999e-05,
 'smoothing_seasonal': nan,
 'damping_trend': nan,
 'initial_level': 1686.0,
 'initial_trend': -95.0,
 'initial_seasons': array([], dtype=float64),
 'use_boxcox': False,
 'lamda': None,
 'remove_bias': False}

• The model is designed to give high importance to recent data (𝛼=0.68857), which is suitable if the time series has moderate changes in its level over time.
• The very low 𝛽 = 0.0001 suggests that the model assumes a very stable trend and is not heavily updating the trend component.

DES_test.shape

(43, 1)

## Prediction on the test data

DES_test['auto_predict'] = model_DES_autofit.forecast(steps=55)
DES_test.head()

	Sparkling	auto_predict
Time_Stamp
1992-01-31	1577	5164.113674
1992-02-29	1667	5070.828157
1992-03-31	1993	4977.542640
1992-04-30	1997	4884.257122
1992-05-31	1783	4790.971605

## Plotting on both the Training and Test using autofit

plt.figure(figsize=(18,9))
plt.plot(DES_train['Sparkling'], label='Train')
plt.plot(DES_test['Sparkling'], label='Test')

plt.plot(DES_test['auto_predict'], label='Alpha = 0.688,Beta = 9.99e-05,DoubleExponentialSmoothing predictions on Test Set')


plt.legend(loc='best')
plt.grid();

• Using double exponantial smoothing predication on test set at 𝛼=0.68857 it show sparkling negative correlation overtime

## Test Data

rmse_model_test = metrics.mean_squared_error(DES_test['Sparkling'],DES_test['auto_predict'],squared=False)
print("For Alpha=0.688,Beta=0.00009,DoubleExponentialSmoothing predictions on Test Data,  RMSE is %3.3f" %(rmse_model_test))

For Alpha=0.688,Beta=0.00009,DoubleExponentialSmoothing predictions on Test Data,  RMSE is 1923.793

• The RMSE of 1923.793 suggests the model has moderate accuracy but struggles with variability in the data.
• The high α prioritizes recent data, while the very low 𝛽 leads to a static trend that may not fully capture changes.

resultsDf_DES = pd.DataFrame({'Test RMSE': [rmse_model_test]}
                           ,index=['DoubleExponentialSmoothing(α=0.688)'])  # Complete the code to check the perfromance of the 'Alpha=0.688,Beta=0.00009,DoubleExponentialSmoothing' 

resultsDf = pd.concat([resultsDf, resultsDf_DES])
resultsDf

	Test RMSE
RegressionOnTime	1337.090220
NaiveModel	3979.914692
SimpleAverageModel	1268.683035
SimpleExponentialSmoothing	1296.358039
SimpleExponentialSmoothing(α=0.3)	1900.058569
DoubleExponentialSmoothing(α=0.688)	1923.793446

## First we will define an empty dataframe to store our values from the loop

resultsDf_b = pd.DataFrame({'Alpha Values':[],'Beta Values':[],'Train RMSE':[],'Test RMSE': []})
resultsDf_b

	Alpha Values	Beta Values	Train RMSE	Test RMSE

len(df_test)

43

for i in np.arange(0.3,1.1,0.1):
    for j in np.arange(0.3,1.1,0.1):
        model_DES_alpha_i_j = model_DES.fit(smoothing_level=i,smoothing_trend=j,optimized=False,use_brute=True)
        DES_train['predict',i,j] = model_DES_alpha_i_j.fittedvalues
        DES_test['predict',i,j] = model_DES_alpha_i_j.forecast(steps=55)
        
        rmse_model_train = metrics.mean_squared_error(DES_train['Sparkling'],DES_train['predict',i,j],squared=False)
        
        rmse_model_test = metrics.mean_squared_error(DES_test['Sparkling'],DES_test['predict',i,j],squared=False)
        
        resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
                                          ,'Test RMSE':rmse_model_test}, ignore_index=True)

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3776927317.py:11: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_b = resultsDf_b.append({'Alpha Values':i,'Beta Values':j,'Train RMSE':rmse_model_train

resultsDf_b.sort_values(by=['Test RMSE']).head()

	Alpha Values	Beta Values	Train RMSE	Test RMSE
0	0.3	0.3	1599.398181	13814.398308
8	0.4	0.3	1578.236948	18044.669982
1	0.3	0.4	1693.819564	19249.257218
16	0.5	0.3	1538.865716	20684.637333
24	0.6	0.3	1513.676637	22536.689632

• By using 𝛼=0.3 and 𝛽=0.3, you achieve the best balance between model simplicity and predictive accuracy for your test dataset.

DES_test.columns

Index([                                        'Sparkling',
                                            'auto_predict',
                                     ('predict', 0.3, 0.3),
                                     ('predict', 0.3, 0.4),
                                     ('predict', 0.3, 0.5),
                      ('predict', 0.3, 0.6000000000000001),
                      ('predict', 0.3, 0.7000000000000002),
                      ('predict', 0.3, 0.8000000000000003),
                      ('predict', 0.3, 0.9000000000000001),
                      ('predict', 0.3, 1.0000000000000002),
                                     ('predict', 0.4, 0.3),
                                     ('predict', 0.4, 0.4),
                                     ('predict', 0.4, 0.5),
                      ('predict', 0.4, 0.6000000000000001),
                      ('predict', 0.4, 0.7000000000000002),
                      ('predict', 0.4, 0.8000000000000003),
                      ('predict', 0.4, 0.9000000000000001),
                      ('predict', 0.4, 1.0000000000000002),
                                     ('predict', 0.5, 0.3),
                                     ('predict', 0.5, 0.4),
                                     ('predict', 0.5, 0.5),
                      ('predict', 0.5, 0.6000000000000001),
                      ('predict', 0.5, 0.7000000000000002),
                      ('predict', 0.5, 0.8000000000000003),
                      ('predict', 0.5, 0.9000000000000001),
                      ('predict', 0.5, 1.0000000000000002),
                      ('predict', 0.6000000000000001, 0.3),
                      ('predict', 0.6000000000000001, 0.4),
                      ('predict', 0.6000000000000001, 0.5),
       ('predict', 0.6000000000000001, 0.6000000000000001),
       ('predict', 0.6000000000000001, 0.7000000000000002),
       ('predict', 0.6000000000000001, 0.8000000000000003),
       ('predict', 0.6000000000000001, 0.9000000000000001),
       ('predict', 0.6000000000000001, 1.0000000000000002),
                      ('predict', 0.7000000000000002, 0.3),
                      ('predict', 0.7000000000000002, 0.4),
                      ('predict', 0.7000000000000002, 0.5),
       ('predict', 0.7000000000000002, 0.6000000000000001),
       ('predict', 0.7000000000000002, 0.7000000000000002),
       ('predict', 0.7000000000000002, 0.8000000000000003),
       ('predict', 0.7000000000000002, 0.9000000000000001),
       ('predict', 0.7000000000000002, 1.0000000000000002),
                      ('predict', 0.8000000000000003, 0.3),
                      ('predict', 0.8000000000000003, 0.4),
                      ('predict', 0.8000000000000003, 0.5),
       ('predict', 0.8000000000000003, 0.6000000000000001),
       ('predict', 0.8000000000000003, 0.7000000000000002),
       ('predict', 0.8000000000000003, 0.8000000000000003),
       ('predict', 0.8000000000000003, 0.9000000000000001),
       ('predict', 0.8000000000000003, 1.0000000000000002),
                      ('predict', 0.9000000000000001, 0.3),
                      ('predict', 0.9000000000000001, 0.4),
                      ('predict', 0.9000000000000001, 0.5),
       ('predict', 0.9000000000000001, 0.6000000000000001),
       ('predict', 0.9000000000000001, 0.7000000000000002),
       ('predict', 0.9000000000000001, 0.8000000000000003),
       ('predict', 0.9000000000000001, 0.9000000000000001),
       ('predict', 0.9000000000000001, 1.0000000000000002),
                      ('predict', 1.0000000000000002, 0.3),
                      ('predict', 1.0000000000000002, 0.4),
                      ('predict', 1.0000000000000002, 0.5),
       ('predict', 1.0000000000000002, 0.6000000000000001),
       ('predict', 1.0000000000000002, 0.7000000000000002),
       ('predict', 1.0000000000000002, 0.8000000000000003),
       ('predict', 1.0000000000000002, 0.9000000000000001),
       ('predict', 1.0000000000000002, 1.0000000000000002)],
      dtype='object')

resultsDf_3 = pd.DataFrame({'Test RMSE': [resultsDf_b.sort_values(by=['Test RMSE']).values[0][3]]}
                           ,index=['Alpha=0.6,Beta=0.0001,DoubleExponentialSmoothing'])   # Complete the code to check the perfromance of the 'Alpha=0.6,Beta=0.00010,DoubleExponentialSmoothing' 

resultsDf = pd.concat([resultsDf, resultsDf_3])
resultsDf

	Test RMSE
RegressionOnTime	1337.090220
NaiveModel	3979.914692
SimpleAverageModel	1268.683035
SimpleExponentialSmoothing	1296.358039
SimpleExponentialSmoothing(α=0.3)	1900.058569
DoubleExponentialSmoothing(α=0.688)	1923.793446
Alpha=0.6,Beta=0.00010,DoubleExponentialSmoothing	13814.398308
Alpha=0.6,Beta=0.3,DoubleExponentialSmoothing	13814.398308

• The highest RMSE, indicating extremely poor performance.
• This parameter combination likely failed to capture both the level and trend of the data, leading to inaccurate predictions.
• The Simple Average Model (RMSE = 1268.683) still the best-performing model until now.

Triple Exponential Smoothing (Holt - Winter's Model)

Three parameters 𝛼 , 𝛽 and 𝛾 are estimated in this model. Level, Trend and Seasonality are accounted for in this model.

# Creating the copy of train and test dataset 
TES_train = df_train.copy() # Complete the code to create a copy of the train dataset
TES_test = df_test.copy() # Complete the code to create a copy of the test dataset

model_TES = ExponentialSmoothing(TES_train['Sparkling'],trend='additive',seasonal='multiplicative',freq='M')

model_TES_autofit = model_TES.fit()

model_TES_autofit.params

{'smoothing_level': 0.0760775317017757,
 'smoothing_trend': 0.07607728744840153,
 'smoothing_seasonal': 0.34206210109518037,
 'damping_trend': nan,
 'initial_level': 2356.3687669747187,
 'initial_trend': -16.605498759558397,
 'initial_seasons': array([0.72013071, 0.68871014, 0.90372652, 0.80520439, 0.65572451,
        0.65102787, 0.88120117, 1.13713477, 0.92418717, 1.22521121,
        1.92183841, 2.44205244]),
 'use_boxcox': False,
 'lamda': None,
 'remove_bias': False}

• The initial_level (2356.37) is close to the dataset mean (2402.42), suggesting that the model captures the central tendency well.

##PREDICTION ON TEST SET
TES_test['auto_predict'] = model_TES_autofit.forecast(steps=len(df_test))
TES_test.head()

	Sparkling	auto_predict
Time_Stamp
1992-01-31	1577	1720.327211
1992-02-29	1667	1612.401776
1992-03-31	1993	1794.921743
1992-04-30	1997	1531.035046
1992-05-31	1783	1508.819520

## Plotting on both the Training and Test using autofit

plt.figure(figsize=(18,9))
plt.plot(TES_train['Sparkling'], label='Train')
plt.plot(TES_test['Sparkling'], label='Test')

plt.plot(TES_test['auto_predict'], label='Alpha=0.111,Beta=0.061,Gamma=0.395,TripleExponentialSmoothing predictions on Test Set')


plt.legend(loc='best')
plt.grid();

## Test Data

rmse_model_test = metrics.mean_squared_error(TES_test['Sparkling'],TES_test['auto_predict'],squared=False)
print("Alpha=0.111,Beta=0.061,Gamma=0.395,TripleExponentialSmoothing predictions on Test Set,  RMSE is %3.3f" %(rmse_model_test))

Alpha=0.111,Beta=0.061,Gamma=0.395,TripleExponentialSmoothing predictions on Test Set,  RMSE is 347.415

• The RMSE is significantly smaller than the dataset's mean (2402.417) and standard deviation (1295.112), suggesting that the model is highly accurate in predicting the test set.
• Compared to other models like RegressionOnTime (RMSE = 1337.090) and Simple Average Model (RMSE = 1268.683), this result is a major improvement.

## First we will define an empty dataframe to store our values from the loop
resultsDf_c = pd.DataFrame({'Alpha Values':[],'Beta Values':[],'Gamma Values':[],'Train RMSE':[],'Test RMSE': []})
resultsDf_c

	Alpha Values	Beta Values	Gamma Values	Train RMSE	Test RMSE

for i in np.arange(0.3,1.1,0.1):
    for j in np.arange(0.3,1.1,0.1):
        for k in np.arange(0.3,1.1,0.1):
            model_TES_alpha_i_j_k = model_TES.fit(smoothing_level=i,smoothing_trend=j,smoothing_seasonal=k,optimized=False,use_brute=True)
            TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
            TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
        
            rmse_model_train = metrics.mean_squared_error(TES_train['Sparkling'],TES_train['predict',i,j,k],squared=False)
            
            rmse_model_test = metrics.mean_squared_error(TES_test['Sparkling'],TES_test['predict',i,j,k],squared=False)
            
            resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
                                                  'Train RMSE':rmse_model_train,'Test RMSE':rmse_model_test}
                                                 , ignore_index=True)

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:5: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_train['predict',i,j,k] = model_TES_alpha_i_j_k.fittedvalues
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`
  TES_test['predict',i,j,k] = model_TES_alpha_i_j_k.forecast(steps=55)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\3778956920.py:12: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  resultsDf_c = resultsDf_c.append({'Alpha Values':i,'Beta Values':j,'Gamma Values':k,

resultsDf_c.sort_values(by=['Test RMSE']).head()

	Alpha Values	Beta Values	Gamma Values	Train RMSE	Test RMSE
128	0.5	0.3	0.3	448.489627	412.613756
194	0.6	0.3	0.5	541.879579	478.752553
72	0.4	0.4	0.3	451.720449	485.608082
260	0.7	0.3	0.7	726.763349	561.382299
81	0.4	0.5	0.4	508.681964	579.245326

• The optimal parameter values are:Alpha (α): 0.5 Beta (β): 0.3 Gamma (γ): 0.3
• This combination provides the best generalization performance (Test RMSE = 412.61) and is recommended for forecasting with Triple Exponential Smoothing.

## Plotting on both the Training and Test data using brute force alpha, beta and gamma determination

plt.figure(figsize=(18,9))
plt.plot(TES_train['Sparkling'], label='Train')
plt.plot(TES_test['Sparkling'], label='Test')

#The value of alpha and beta is taken like that by python
plt.plot(TES_test['predict', 0.5, 0.3, 0.3], label='Alpha=0.3,Beta=0.3,Gamma=0.3,TripleExponentialSmoothing predictions on Test Set')


plt.legend(loc='best')
plt.grid();

resultsDf_2 = pd.DataFrame({'Test RMSE': [resultsDf_c.sort_values(by=['Test RMSE']).values[0][4]]}
                           ,index=['Alpha= 0.5,Beta=0.3,Gamma=0.3,TripleExponentialSmoothing'])  # Complete the code to check the perfromance of the 'Alpha= 0.3,Beta=0.3,Gamma=0.3,TripleExponentialSmoothing' 


resultsDf = pd.concat([resultsDf, resultsDf_2])
resultsDf

	Test RMSE
RegressionOnTime	1337.090220
NaiveModel	3979.914692
SimpleAverageModel	1268.683035
SimpleExponentialSmoothing	1296.358039
SimpleExponentialSmoothing(α=0.3)	1900.058569
DoubleExponentialSmoothing(α=0.688)	1923.793446
Alpha=0.6,Beta=0.00010,DoubleExponentialSmoothing	13814.398308
Alpha=0.6,Beta=0.3,DoubleExponentialSmoothing	13814.398308
Alpha= 0.3,Beta=0.3,Gamma=0.3,TripleExponentialSmoothing	412.613756
Alpha= 0.5,Beta=0.3,Gamma=0.3,TripleExponentialSmoothing	412.613756

resultsDf1 = resultsDf.sort_values(by=['Test RMSE'])
resultsDf1

	Test RMSE
Alpha= 0.3,Beta=0.3,Gamma=0.3,TripleExponentialSmoothing	412.613756
Alpha= 0.5,Beta=0.3,Gamma=0.3,TripleExponentialSmoothing	412.613756
SimpleAverageModel	1268.683035
SimpleExponentialSmoothing	1296.358039
RegressionOnTime	1337.090220
SimpleExponentialSmoothing(α=0.3)	1900.058569
DoubleExponentialSmoothing(α=0.688)	1923.793446
NaiveModel	3979.914692
Alpha=0.6,Beta=0.00010,DoubleExponentialSmoothing	13814.398308
Alpha=0.6,Beta=0.3,DoubleExponentialSmoothing	13814.398308

• The best model for your dataset is Triple Exponential Smoothing (Holt-Winter's Model) with α = 0.3 or 0.5, β = 0.3, γ = 0.3, achieving a Test RMSE of 412.61,

Checking for Stationarity

Let's check for the stationarity of the data on which the model is being built on using appropriate statistical tests and also mention the hypothesis for the statistical test. If the data is found to be non-stationary, take appropriate steps to make it stationary. Check the new data for stationarity and comment. Note: Stationarity should be checked at alpha = 0.05.

from statsmodels.tsa.stattools import adfuller
def test_stationarity(timeseries):
    
    #determining roll statistics
    rolmean = timeseries.rolling(window=12).mean()
    rolstd = timeseries.rolling(window=12).std()
    
    ##plot rolling Statistics:
    orig = plt.plot(timeseries,color='blue',label='Original')
    mean = plt.plot(rolmean, color='red', label='Rolling Mean')
    std = plt.plot(rolstd, color='black', label='Rolling Std')
    plt.legend(loc='best')
    plt.title('Rolling Mean and Standard Deviation')
    plt.show(block=False)
    
    #Perform Dickey-Fuller Test:
    print('Results of Dickey Fuller Test:')
    dftest = adfuller(timeseries, autolag='AIC')
    dfoutput = pd.Series(dftest[0:4], index=['Test Statistic','p-value','#Lags Used','Number of Observations Used'])
    for key,value in dftest[4].items():
        dfoutput['Critical Value (%s)'%key] = value
    print(dfoutput,'\n')

test_stationarity(df_train['Sparkling'])

Results of Dickey Fuller Test:
Test Statistic                  -1.265771
p-value                          0.644683
#Lags Used                      12.000000
Number of Observations Used    131.000000
Critical Value (1%)             -3.481282
Critical Value (5%)             -2.883868
Critical Value (10%)            -2.578677
dtype: float64 

Write the Observations

H0 : The series is not stationary

Ha: The series is Stationary

The p-value = 0.644683 is much greater than the typical significance level (e.g., 0.05 or 0.01), so we fail to reject the null hypothesis.

---

Note:

• autolag{“AIC”, “BIC”, “t-stat”, None} Method to use when automatically determining the lag length among the values 0, 1, …, maxlag.

• If “AIC” (default) or “BIC”, then the number of lags is chosen to minimize the corresponding information criterion.

• “t-stat” based choice of maxlag. Starts with maxlag and drops a lag until the t-statistic on the last lag length is significant using a 5%-sized test.

• If None, then the number of included lags is set to maxlag.

• The null hypothesis of the Augmented Dickey-Fuller is that there is a unit root, with the alternative that there is no unit root. If the pvalue is above a critical size, then we cannot reject that there is a unit root.

Stationary TS allow us to essentially have copies of things which enables us to build appropriate statistical models for forecasting

LETS BUILD IT ON DF_TRAIN MODEL

dftest = adfuller(df_train.diff().dropna(),regression='ct')
print('DF test statistics is %3.3f' %dftest[0])
print('DF test p-value is', dftest[1])
print('DF test p-value is', dftest[2])

DF test statistics is -8.628
DF test p-value is 2.5490597847951888e-12
DF test p-value is 11

• The p-value = 2.55×10e−12 is much smaller than the typical significance level (𝛼=.05), meaning we reject the null hypothesis.
• Conclusion: The time series is stationary after differencing.

df_train.plot(grid=True);

Automated ARIMA Model based on lowest AIC

Let's build an automated version of the ARIMA model in which the parameters are selected using the lowest Akaike Information Criteria (AIC) on the training data and evaluate this model on the test data using RMSE.

import itertools
p = q = range(0,4)
d = range(1,2)
pdq = list(itertools.product(p,d,q))
print('Examples of the parameter combinations for the models')
for i in range(0,len(pdq)):
    print('Model : {}'.format(pdq[i]))

Examples of the parameter combinations for the models
Model : (0, 1, 0)
Model : (0, 1, 1)
Model : (0, 1, 2)
Model : (0, 1, 3)
Model : (1, 1, 0)
Model : (1, 1, 1)
Model : (1, 1, 2)
Model : (1, 1, 3)
Model : (2, 1, 0)
Model : (2, 1, 1)
Model : (2, 1, 2)
Model : (2, 1, 3)
Model : (3, 1, 0)
Model : (3, 1, 1)
Model : (3, 1, 2)
Model : (3, 1, 3)

# Creating an empty Dataframe with column names only
ARIMA_AIC = pd.DataFrame(columns=['param', 'AIC'])
ARIMA_AIC

	param	AIC

from statsmodels.tsa.arima.model import ARIMA

for param in pdq: # running a loop within the pdq parameters defined by itertools
    ARIMA_model  =  ARIMA(df_train['Sparkling'].values, order=param).fit()
    print('ARIMA{} - AIC{}'.format(param, ARIMA_model.aic))
    
    #printing the parameters and the AIC from the fitted models
    ARIMA_AIC = ARIMA_AIC.append({'param': param, 'AIC': ARIMA_model.aic},ignore_index=True)
    
    #appending the AIC values and the model parameters to the previously created data frame
    #for easier understanding and sorting of the AIC values

ARIMA(0, 1, 0) - AIC2476.745546947
ARIMA(0, 1, 1) - AIC2470.9032654144103

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1463336305.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  ARIMA_AIC = ARIMA_AIC.append({'param': param, 'AIC': ARIMA_model.aic},ignore_index=True)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1463336305.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  ARIMA_AIC = ARIMA_AIC.append({'param': param, 'AIC': ARIMA_model.aic},ignore_index=True)

ARIMA(0, 1, 2) - AIC2439.4718189796276
ARIMA(0, 1, 3) - AIC2439.127183835587

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1463336305.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  ARIMA_AIC = ARIMA_AIC.append({'param': param, 'AIC': ARIMA_model.aic},ignore_index=True)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1463336305.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  ARIMA_AIC = ARIMA_AIC.append({'param': param, 'AIC': ARIMA_model.aic},ignore_index=True)

ARIMA(1, 1, 0) - AIC2474.923642766008
ARIMA(1, 1, 1) - AIC2440.4861127564113

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1463336305.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  ARIMA_AIC = ARIMA_AIC.append({'param': param, 'AIC': ARIMA_model.aic},ignore_index=True)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1463336305.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  ARIMA_AIC = ARIMA_AIC.append({'param': param, 'AIC': ARIMA_model.aic},ignore_index=True)

ARIMA(1, 1, 2) - AIC2439.712391776987

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1463336305.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  ARIMA_AIC = ARIMA_AIC.append({'param': param, 'AIC': ARIMA_model.aic},ignore_index=True)

ARIMA(1, 1, 3) - AIC2440.808627826275
ARIMA(2, 1, 0) - AIC2468.7139871180234

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1463336305.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  ARIMA_AIC = ARIMA_AIC.append({'param': param, 'AIC': ARIMA_model.aic},ignore_index=True)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1463336305.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  ARIMA_AIC = ARIMA_AIC.append({'param': param, 'AIC': ARIMA_model.aic},ignore_index=True)

ARIMA(2, 1, 1) - AIC2438.8717349509843

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1463336305.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  ARIMA_AIC = ARIMA_AIC.append({'param': param, 'AIC': ARIMA_model.aic},ignore_index=True)

ARIMA(2, 1, 2) - AIC2419.166998310233

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1463336305.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  ARIMA_AIC = ARIMA_AIC.append({'param': param, 'AIC': ARIMA_model.aic},ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1463336305.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  ARIMA_AIC = ARIMA_AIC.append({'param': param, 'AIC': ARIMA_model.aic},ignore_index=True)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1463336305.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  ARIMA_AIC = ARIMA_AIC.append({'param': param, 'AIC': ARIMA_model.aic},ignore_index=True)

ARIMA(2, 1, 3) - AIC2438.3644928591807
ARIMA(3, 1, 0) - AIC2466.346553693917
ARIMA(3, 1, 1) - AIC2440.4277709016706

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1463336305.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  ARIMA_AIC = ARIMA_AIC.append({'param': param, 'AIC': ARIMA_model.aic},ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\tsa\statespace\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.
  warn('Non-stationary starting autoregressive parameters'
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
  warn('Non-invertible starting MA parameters found.'
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1463336305.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  ARIMA_AIC = ARIMA_AIC.append({'param': param, 'AIC': ARIMA_model.aic},ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\tsa\statespace\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.
  warn('Non-invertible starting MA parameters found.'

ARIMA(3, 1, 2) - AIC2435.456935737263
ARIMA(3, 1, 3) - AIC2427.5078516769345

C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1463336305.py:8: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  ARIMA_AIC = ARIMA_AIC.append({'param': param, 'AIC': ARIMA_model.aic},ignore_index=True)

ARIMA_AIC.sort_values(by='AIC',ascending=True).head()

	param	AIC
10	(2, 1, 2)	2419.166998
15	(3, 1, 3)	2427.507852
14	(3, 1, 2)	2435.456936
11	(2, 1, 3)	2438.364493
9	(2, 1, 1)	2438.871735

• The model with the lowest AIC is ARIMA(2, 1, 2), with an AIC value of 2419.17.
• This suggests that p = 2, d = 1, and q = 2 is the optimal parameter combination among the tested models.

auto_ARIMA = ARIMA(df_train, order=(2,1,2))

results_auto_ARIMA = auto_ARIMA.fit()

print(results_auto_ARIMA.summary())

C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)

                               SARIMAX Results                                
==============================================================================
Dep. Variable:              Sparkling   No. Observations:                  144
Model:                 ARIMA(2, 1, 2)   Log Likelihood               -1204.583
Date:                Sun, 19 Jan 2025   AIC                           2419.167
Time:                        10:52:45   BIC                           2433.981
Sample:                    01-31-1980   HQIC                          2425.187
                         - 12-31-1991                                         
Covariance Type:                  opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1          1.3214      0.043     30.410      0.000       1.236       1.407
ar.L2         -0.5501      0.062     -8.906      0.000      -0.671      -0.429
ma.L1         -1.9912      0.105    -18.931      0.000      -2.197      -1.785
ma.L2          0.9995      0.106      9.473      0.000       0.793       1.206
sigma2      1.136e+06   1.88e-07   6.05e+12      0.000    1.14e+06    1.14e+06
===================================================================================
Ljung-Box (L1) (Q):                   0.07   Jarque-Bera (JB):                15.12
Prob(Q):                              0.80   Prob(JB):                         0.00
Heteroskedasticity (H):               2.03   Skew:                             0.61
Prob(H) (two-sided):                  0.02   Kurtosis:                         4.03
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
[2] Covariance matrix is singular or near-singular, with condition number 1.27e+28. Standard errors may be unstable.

• The ARIMA(2, 1, 2) model captures the short-term dependencies (autoregressive terms) and accounts for recent forecast errors (moving average terms). However The high variance of residuals (sigma²) and heteroskedasticity indicate that the model may not fully capture some aspects of the data's structure.

results_auto_ARIMA.plot_diagnostics()

• The diagnostic plots and statistics suggest that the ARIMA(2,1,2) model fits the data well, capturing autocorrelation and leaving residuals uncorrelated. However, slight deviations from normality and outliers may need further investigation, especially if they impact model predictions or forecast accuracy.

Predict on the Test Set using this model and evaluate the model.

predicted_auto_ARIMA = results_auto_ARIMA.forecast(steps=len(df_test))

## Mean Absolute Percentage Error (MAPE) - Function Definition

def mean_absolute_percentage_error(y_true, y_pred):
    return np.mean((np.abs(y_true-y_pred))/(y_true))*100

## Importing the mean_squared_error function from sklearn to calculate the RMSE

from sklearn.metrics import mean_squared_error

rmse = mean_squared_error(df_test['Sparkling'],predicted_auto_ARIMA,squared=False)
mape = mean_absolute_percentage_error(df_test['Sparkling'],predicted_auto_ARIMA)
print('RMSE:',rmse,'\nMAPE:',mape)

RMSE: 1309.6316772059226 
MAPE: 42.106202366124656

from math import sqrt
from sklearn.metrics import  mean_squared_error
rmse = sqrt(mean_squared_error(df_test.Sparkling,predicted_auto_ARIMA))
print(rmse)

1309.6316772059226

resultsDf = pd.DataFrame({'RMSE': rmse,'MAPE':mape}
                           ,index=['ARIMA(2,1,2)'])

resultsDf

	RMSE	MAPE
ARIMA(2,1,2)	1309.631677	42.106202

• A MAPE of 42.11% highlights challenges with model accuracy, particularly in predicting percentage changes.
• Residual diagnostics show deviations from normality and some outliers, which may affect prediction performance.
• RMSE is comparable to the standard deviation, indicating the model captures variability reasonably but not perfectly.

Automated SARIMA Model based on lowest AIC

Let's build an automated version of the SARIMA model in which the parameters are selected using the lowest Akaike Information Criteria (AIC) on the training data and evaluate this model on the test data using RMSE.

plot_acf(df_train.diff(),title='Training Data Autocorrelation',missing='drop',lags=40);

import itertools
p = q = range(0, 4)
d= range(1,2)
D = range(0,1)
pdq = list(itertools.product(p, d, q))
PDQ = [(x[0], x[1], x[2], 12) for x in list(itertools.product(p, D, q))]
print('Examples of the parameter combinations for the Model are')
for i in range(1,len(pdq)):
    print('Model: {}{}'.format(pdq[i], PDQ[i]))

Examples of the parameter combinations for the Model are
Model: (0, 1, 1)(0, 0, 1, 12)
Model: (0, 1, 2)(0, 0, 2, 12)
Model: (0, 1, 3)(0, 0, 3, 12)
Model: (1, 1, 0)(1, 0, 0, 12)
Model: (1, 1, 1)(1, 0, 1, 12)
Model: (1, 1, 2)(1, 0, 2, 12)
Model: (1, 1, 3)(1, 0, 3, 12)
Model: (2, 1, 0)(2, 0, 0, 12)
Model: (2, 1, 1)(2, 0, 1, 12)
Model: (2, 1, 2)(2, 0, 2, 12)
Model: (2, 1, 3)(2, 0, 3, 12)
Model: (3, 1, 0)(3, 0, 0, 12)
Model: (3, 1, 1)(3, 0, 1, 12)
Model: (3, 1, 2)(3, 0, 2, 12)
Model: (3, 1, 3)(3, 0, 3, 12)

SARIMA_AIC = pd.DataFrame(columns=['param','seasonal', 'AIC'])
SARIMA_AIC

	param	seasonal	AIC

import statsmodels.api as sm

for param in pdq:
    for param_seasonal in PDQ:
        SARIMA_model = sm.tsa.statespace.SARIMAX(df_train['Sparkling'].values,
                                            order=param,
                                            seasonal_order=param_seasonal,
                                            enforce_stationarity=False,
                                            enforce_invertibility=False)
            
        results_SARIMA = SARIMA_model.fit(maxiter=1000)
        print('SARIMA{}x{} - AIC:{}'.format(param, param_seasonal, results_SARIMA.aic))
        SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(0, 1, 0)x(0, 0, 0, 12) - AIC:2460.4391566410877
SARIMA(0, 1, 0)x(0, 0, 1, 12) - AIC:2153.4499863142355

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(0, 1, 0)x(0, 0, 2, 12) - AIC:1913.3324130187623

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

SARIMA(0, 1, 0)x(0, 0, 3, 12) - AIC:7736.558286878177
SARIMA(0, 1, 0)x(1, 0, 0, 12) - AIC:2019.7175595892088
SARIMA(0, 1, 0)x(1, 0, 1, 12) - AIC:1989.778581372794

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(0, 1, 0)x(1, 0, 2, 12) - AIC:1814.4132836398865

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(0, 1, 0)x(1, 0, 3, 12) - AIC:3170.2310499644864
SARIMA(0, 1, 0)x(2, 0, 0, 12) - AIC:1830.0102939478718
SARIMA(0, 1, 0)x(2, 0, 1, 12) - AIC:1830.0003655676471
SARIMA(0, 1, 0)x(2, 0, 2, 12) - AIC:1815.8048034159779

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(0, 1, 0)x(2, 0, 3, 12) - AIC:3925.930246833829
SARIMA(0, 1, 0)x(3, 0, 0, 12) - AIC:1651.6072552741205
SARIMA(0, 1, 0)x(3, 0, 1, 12) - AIC:1653.4767876481185

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(0, 1, 0)x(3, 0, 2, 12) - AIC:1654.8722679500472

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(0, 1, 0)x(3, 0, 3, 12) - AIC:4248.639442505446
SARIMA(0, 1, 1)x(0, 0, 0, 12) - AIC:2437.9469723862794
SARIMA(0, 1, 1)x(0, 0, 1, 12) - AIC:2122.815214759641

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(0, 1, 1)x(0, 0, 2, 12) - AIC:1879.9340913721007

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(0, 1, 1)x(0, 0, 3, 12) - AIC:7666.628947422329
SARIMA(0, 1, 1)x(1, 0, 0, 12) - AIC:1972.078873375368

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(0, 1, 1)x(1, 0, 1, 12) - AIC:1917.6448104928375
SARIMA(0, 1, 1)x(1, 0, 2, 12) - AIC:1749.380573458138

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(0, 1, 1)x(1, 0, 3, 12) - AIC:3228.7139413047853
SARIMA(0, 1, 1)x(2, 0, 0, 12) - AIC:1782.2937723723971

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(0, 1, 1)x(2, 0, 1, 12) - AIC:1778.614013015443

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(0, 1, 1)x(2, 0, 2, 12) - AIC:1750.0719103461688

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(0, 1, 1)x(2, 0, 3, 12) - AIC:3423.719077320342
SARIMA(0, 1, 1)x(3, 0, 0, 12) - AIC:1606.8038578319574

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(0, 1, 1)x(3, 0, 1, 12) - AIC:1608.4054072890776

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(0, 1, 1)x(3, 0, 2, 12) - AIC:1609.4754899003722

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(0, 1, 1)x(3, 0, 3, 12) - AIC:3427.6911885866393
SARIMA(0, 1, 2)x(0, 0, 0, 12) - AIC:2392.7436964785766
SARIMA(0, 1, 2)x(0, 0, 1, 12) - AIC:2086.229485518489

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(0, 1, 2)x(0, 0, 2, 12) - AIC:1847.1669919492779

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(0, 1, 2)x(0, 0, 3, 12) - AIC:7592.594051185987
SARIMA(0, 1, 2)x(1, 0, 0, 12) - AIC:1964.4202716174445

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(0, 1, 2)x(1, 0, 1, 12) - AIC:1901.3324565638677

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(0, 1, 2)x(1, 0, 2, 12) - AIC:1733.9753420990967

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(0, 1, 2)x(1, 0, 3, 12) - AIC:3865.8479597535134
SARIMA(0, 1, 2)x(2, 0, 0, 12) - AIC:1777.8175657592794

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(0, 1, 2)x(2, 0, 1, 12) - AIC:1777.172895358724

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(0, 1, 2)x(2, 0, 2, 12) - AIC:1734.445405430158

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(0, 1, 2)x(2, 0, 3, 12) - AIC:4071.439172329453
SARIMA(0, 1, 2)x(3, 0, 0, 12) - AIC:1604.4943869311664

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(0, 1, 2)x(3, 0, 1, 12) - AIC:1606.3471395952924

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(0, 1, 2)x(3, 0, 2, 12) - AIC:1607.9857271054689

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(0, 1, 2)x(3, 0, 3, 12) - AIC:4090.7735468714886
SARIMA(0, 1, 3)x(0, 0, 0, 12) - AIC:2374.6776328692195
SARIMA(0, 1, 3)x(0, 0, 1, 12) - AIC:2072.0398134889556

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(0, 1, 3)x(0, 0, 2, 12) - AIC:1831.9543283494784

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(0, 1, 3)x(0, 0, 3, 12) - AIC:2560.692642479879
SARIMA(0, 1, 3)x(1, 0, 0, 12) - AIC:1966.3365287368124

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(0, 1, 3)x(1, 0, 1, 12) - AIC:1887.3250749955146

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(0, 1, 3)x(1, 0, 2, 12) - AIC:1719.120791325296

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(0, 1, 3)x(1, 0, 3, 12) - AIC:3671.393770543239
SARIMA(0, 1, 3)x(2, 0, 0, 12) - AIC:1778.5020512955862

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(0, 1, 3)x(2, 0, 1, 12) - AIC:1777.5814650644572

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(0, 1, 3)x(2, 0, 2, 12) - AIC:1719.9490454184013

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(0, 1, 3)x(2, 0, 3, 12) - AIC:3867.0679254891743
SARIMA(0, 1, 3)x(3, 0, 0, 12) - AIC:1604.840596891594

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(0, 1, 3)x(3, 0, 1, 12) - AIC:1615.1376510495127

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(0, 1, 3)x(3, 0, 2, 12) - AIC:1607.8959651220775

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(0, 1, 3)x(3, 0, 3, 12) - AIC:3869.0109336901696
SARIMA(1, 1, 0)x(0, 0, 0, 12) - AIC:2458.624148794397
SARIMA(1, 1, 0)x(0, 0, 1, 12) - AIC:2151.913471416122
SARIMA(1, 1, 0)x(0, 0, 2, 12) - AIC:1910.748419991556

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(1, 1, 0)x(0, 0, 3, 12) - AIC:7740.669177833487
SARIMA(1, 1, 0)x(1, 0, 0, 12) - AIC:1992.391512768205
SARIMA(1, 1, 0)x(1, 0, 1, 12) - AIC:1972.5427510742556

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(1, 1, 0)x(1, 0, 2, 12) - AIC:1800.7835498683557

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(1, 1, 0)x(1, 0, 3, 12) - AIC:3956.0104227365946
SARIMA(1, 1, 0)x(2, 0, 0, 12) - AIC:1803.4501723395315
SARIMA(1, 1, 0)x(2, 0, 1, 12) - AIC:1801.4441167811785

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(1, 1, 0)x(2, 0, 2, 12) - AIC:1801.1304633575191

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(1, 1, 0)x(2, 0, 3, 12) - AIC:4156.161078096781
SARIMA(1, 1, 0)x(3, 0, 0, 12) - AIC:1625.5224208200887

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(1, 1, 0)x(3, 0, 1, 12) - AIC:1627.4151709288737

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(1, 1, 0)x(3, 0, 2, 12) - AIC:1628.9691474768551

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(1, 1, 0)x(3, 0, 3, 12) - AIC:4159.295083838122
SARIMA(1, 1, 1)x(0, 0, 0, 12) - AIC:2410.0884627275573
SARIMA(1, 1, 1)x(0, 0, 1, 12) - AIC:2104.0129508091877

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(1, 1, 1)x(0, 0, 2, 12) - AIC:1864.1374831824196

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(1, 1, 1)x(0, 0, 3, 12) - AIC:3371.938660800721
SARIMA(1, 1, 1)x(1, 0, 0, 12) - AIC:1949.8127631915477
SARIMA(1, 1, 1)x(1, 0, 1, 12) - AIC:1917.6915508145426

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(1, 1, 1)x(1, 0, 2, 12) - AIC:1748.9115708430454

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(1, 1, 1)x(1, 0, 3, 12) - AIC:2741.138894121306
SARIMA(1, 1, 1)x(2, 0, 0, 12) - AIC:1765.438703039601

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(1, 1, 1)x(2, 0, 1, 12) - AIC:1821.524510286976

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(1, 1, 1)x(2, 0, 2, 12) - AIC:1749.6504531323712

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(1, 1, 1)x(2, 0, 3, 12) - AIC:3395.103925062856
SARIMA(1, 1, 1)x(3, 0, 0, 12) - AIC:1591.6112831969194

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(1, 1, 1)x(3, 0, 1, 12) - AIC:1593.4430502620164

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(1, 1, 1)x(3, 0, 2, 12) - AIC:1595.0884305488437

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(1, 1, 1)x(3, 0, 3, 12) - AIC:3968.681048006381
SARIMA(1, 1, 2)x(0, 0, 0, 12) - AIC:2389.7064800921275
SARIMA(1, 1, 2)x(0, 0, 1, 12) - AIC:2087.9254267192737

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(1, 1, 2)x(0, 0, 2, 12) - AIC:1846.4490800555457

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(1, 1, 2)x(0, 0, 3, 12) - AIC:2488.4218970691054
SARIMA(1, 1, 2)x(1, 0, 0, 12) - AIC:1945.4280054907117

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(1, 1, 2)x(1, 0, 1, 12) - AIC:1900.84400774599

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(1, 1, 2)x(1, 0, 2, 12) - AIC:1732.0347524447334

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(1, 1, 2)x(1, 0, 3, 12) - AIC:2816.942205274622
SARIMA(1, 1, 2)x(2, 0, 0, 12) - AIC:1762.3269012066187

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(1, 1, 2)x(2, 0, 1, 12) - AIC:1761.8316809174598

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(1, 1, 2)x(2, 0, 2, 12) - AIC:1732.944772558156

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(1, 1, 2)x(2, 0, 3, 12) - AIC:2290.535003712238
SARIMA(1, 1, 2)x(3, 0, 0, 12) - AIC:1589.3127233303567

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(1, 1, 2)x(3, 0, 1, 12) - AIC:1591.2745630063318

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(1, 1, 2)x(3, 0, 2, 12) - AIC:1592.9373880966543

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(1, 1, 2)x(3, 0, 3, 12) - AIC:3644.326322619033
SARIMA(1, 1, 3)x(0, 0, 0, 12) - AIC:2374.9830777484476

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(1, 1, 3)x(0, 0, 1, 12) - AIC:2069.1041460100478

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(1, 1, 3)x(0, 0, 2, 12) - AIC:1827.841110058576

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(1, 1, 3)x(0, 0, 3, 12) - AIC:7513.610346303478
SARIMA(1, 1, 3)x(1, 0, 0, 12) - AIC:1947.46225827556

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(1, 1, 3)x(1, 0, 1, 12) - AIC:1888.8566118155038

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(1, 1, 3)x(1, 0, 2, 12) - AIC:1719.188841045211

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(1, 1, 3)x(1, 0, 3, 12) - AIC:3206.924513251405
SARIMA(1, 1, 3)x(2, 0, 0, 12) - AIC:1764.25730837763

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(1, 1, 3)x(2, 0, 1, 12) - AIC:1763.7728383498068

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(1, 1, 3)x(2, 0, 2, 12) - AIC:1720.3195534113383

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(1, 1, 3)x(2, 0, 3, 12) - AIC:3821.3283089046326
SARIMA(1, 1, 3)x(3, 0, 0, 12) - AIC:1591.3120545792917

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(1, 1, 3)x(3, 0, 1, 12) - AIC:1593.1380111556246

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(1, 1, 3)x(3, 0, 2, 12) - AIC:1594.9453586143

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(1, 1, 3)x(3, 0, 3, 12) - AIC:3742.9364414141023
SARIMA(2, 1, 0)x(0, 0, 0, 12) - AIC:2435.649959372059
SARIMA(2, 1, 0)x(0, 0, 1, 12) - AIC:2144.874957709288

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(2, 1, 0)x(0, 0, 2, 12) - AIC:1901.4803235044517

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(2, 1, 0)x(0, 0, 3, 12) - AIC:7736.941951598704
SARIMA(2, 1, 0)x(1, 0, 0, 12) - AIC:1961.619609298185
SARIMA(2, 1, 0)x(1, 0, 1, 12) - AIC:1941.6111848832134

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(2, 1, 0)x(1, 0, 2, 12) - AIC:1784.966733101205

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(2, 1, 0)x(1, 0, 3, 12) - AIC:3725.044374677721
SARIMA(2, 1, 0)x(2, 0, 0, 12) - AIC:1774.1144710902195

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(2, 1, 0)x(2, 0, 1, 12) - AIC:1772.344666819349

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(2, 1, 0)x(2, 0, 2, 12) - AIC:1770.7613838598343

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(2, 1, 0)x(2, 0, 3, 12) - AIC:3914.313552384413

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(2, 1, 0)x(3, 0, 0, 12) - AIC:1596.94605368483

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(2, 1, 0)x(3, 0, 1, 12) - AIC:1598.62139316676

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(2, 1, 0)x(3, 0, 2, 12) - AIC:1599.1944829406445

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(2, 1, 0)x(3, 0, 3, 12) - AIC:3597.1285068684138
SARIMA(2, 1, 1)x(0, 0, 0, 12) - AIC:2408.429360384621
SARIMA(2, 1, 1)x(0, 0, 1, 12) - AIC:2103.5331430917645

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(2, 1, 1)x(0, 0, 2, 12) - AIC:1862.2497960981998

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(2, 1, 1)x(0, 0, 3, 12) - AIC:14.0
SARIMA(2, 1, 1)x(1, 0, 0, 12) - AIC:1973.6400738430707

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(2, 1, 1)x(1, 0, 1, 12) - AIC:1917.209385632076

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(2, 1, 1)x(1, 0, 2, 12) - AIC:1748.7504631345582

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(2, 1, 1)x(1, 0, 3, 12) - AIC:3394.8512472880407
SARIMA(2, 1, 1)x(2, 0, 0, 12) - AIC:1751.698566258214

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(2, 1, 1)x(2, 0, 1, 12) - AIC:1750.0341243344562

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(2, 1, 1)x(2, 0, 2, 12) - AIC:1749.3790845701224

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(2, 1, 1)x(2, 0, 3, 12) - AIC:3373.2981317770855
SARIMA(2, 1, 1)x(3, 0, 0, 12) - AIC:1578.2212367924762

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(2, 1, 1)x(3, 0, 1, 12) - AIC:1580.2109506811382

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(2, 1, 1)x(3, 0, 2, 12) - AIC:1601.173132966918

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(2, 1, 1)x(3, 0, 3, 12) - AIC:4413.585516186368
SARIMA(2, 1, 2)x(0, 0, 0, 12) - AIC:2370.982698358752

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(2, 1, 2)x(0, 0, 1, 12) - AIC:2088.1841782032157

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(2, 1, 2)x(0, 0, 2, 12) - AIC:1848.4427820531214

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(2, 1, 2)x(0, 0, 3, 12) - AIC:16.0
SARIMA(2, 1, 2)x(1, 0, 0, 12) - AIC:1931.137047876182

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(2, 1, 2)x(1, 0, 1, 12) - AIC:1902.8131908163205

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(2, 1, 2)x(1, 0, 2, 12) - AIC:1734.032674177344

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(2, 1, 2)x(1, 0, 3, 12) - AIC:3640.940092994663
SARIMA(2, 1, 2)x(2, 0, 0, 12) - AIC:1750.2422707699332

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(2, 1, 2)x(2, 0, 1, 12) - AIC:1750.0164284403816

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(2, 1, 2)x(2, 0, 2, 12) - AIC:1734.9396244780244

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(2, 1, 2)x(2, 0, 3, 12) - AIC:3893.331610057794
SARIMA(2, 1, 2)x(3, 0, 0, 12) - AIC:1616.85040125673

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(2, 1, 2)x(3, 0, 1, 12) - AIC:1578.941826286308

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(2, 1, 2)x(3, 0, 2, 12) - AIC:1580.7015447569977

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(2, 1, 2)x(3, 0, 3, 12) - AIC:3728.648962206613
SARIMA(2, 1, 3)x(0, 0, 0, 12) - AIC:2377.957378876878
SARIMA(2, 1, 3)x(0, 0, 1, 12) - AIC:2060.1533591980315

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(2, 1, 3)x(0, 0, 2, 12) - AIC:1823.6483303976254

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(2, 1, 3)x(0, 0, 3, 12) - AIC:18.0
SARIMA(2, 1, 3)x(1, 0, 0, 12) - AIC:1932.9344057684111

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(2, 1, 3)x(1, 0, 1, 12) - AIC:1885.8990792803738

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(2, 1, 3)x(1, 0, 2, 12) - AIC:1717.157576965034

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(2, 1, 3)x(1, 0, 3, 12) - AIC:3863.637849053858
SARIMA(2, 1, 3)x(2, 0, 0, 12) - AIC:1751.9524243152648

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(2, 1, 3)x(2, 0, 1, 12) - AIC:1754.0395054664202

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(2, 1, 3)x(2, 0, 2, 12) - AIC:1733.9766562512843

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(2, 1, 3)x(2, 0, 3, 12) - AIC:3467.2467193554735
SARIMA(2, 1, 3)x(3, 0, 0, 12) - AIC:1581.276388355275

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(2, 1, 3)x(3, 0, 1, 12) - AIC:1580.6761970278346

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(2, 1, 3)x(3, 0, 2, 12) - AIC:1582.5716561422398

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(2, 1, 3)x(3, 0, 3, 12) - AIC:4212.546033129062
SARIMA(3, 1, 0)x(0, 0, 0, 12) - AIC:2417.0123227002114
SARIMA(3, 1, 0)x(0, 0, 1, 12) - AIC:2144.7897942267864

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(3, 1, 0)x(0, 0, 2, 12) - AIC:1899.147023053573

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(3, 1, 0)x(0, 0, 3, 12) - AIC:7736.3791965710825
SARIMA(3, 1, 0)x(1, 0, 0, 12) - AIC:1943.0539710381574

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(3, 1, 0)x(1, 0, 1, 12) - AIC:1924.150443462501

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(3, 1, 0)x(1, 0, 2, 12) - AIC:1783.3322075435085

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(3, 1, 0)x(1, 0, 3, 12) - AIC:3540.0560590766618
SARIMA(3, 1, 0)x(2, 0, 0, 12) - AIC:1759.5124321044048

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(3, 1, 0)x(2, 0, 1, 12) - AIC:1756.8874659732123

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(3, 1, 0)x(2, 0, 2, 12) - AIC:1755.5405791249955

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(3, 1, 0)x(2, 0, 3, 12) - AIC:4004.8861086781358
SARIMA(3, 1, 0)x(3, 0, 0, 12) - AIC:1580.849777004902

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(3, 1, 0)x(3, 0, 1, 12) - AIC:1582.2409475943919

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(3, 1, 0)x(3, 0, 2, 12) - AIC:1582.4254490586072

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(3, 1, 0)x(3, 0, 3, 12) - AIC:3664.625012154448
SARIMA(3, 1, 1)x(0, 0, 0, 12) - AIC:2390.310360066785

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(3, 1, 1)x(0, 0, 1, 12) - AIC:2105.4373468094345

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(3, 1, 1)x(0, 0, 2, 12) - AIC:1864.202673264885

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(3, 1, 1)x(0, 0, 3, 12) - AIC:7666.33201230038
SARIMA(3, 1, 1)x(1, 0, 0, 12) - AIC:1920.56576124557

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(3, 1, 1)x(1, 0, 1, 12) - AIC:1904.4321878979636

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(3, 1, 1)x(1, 0, 2, 12) - AIC:1750.576070219919

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(3, 1, 1)x(1, 0, 3, 12) - AIC:3186.609643705996
SARIMA(3, 1, 1)x(2, 0, 0, 12) - AIC:1739.4368150027497

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(3, 1, 1)x(2, 0, 1, 12) - AIC:1737.9669542312336

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(3, 1, 1)x(2, 0, 2, 12) - AIC:1761.060158837461

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(3, 1, 1)x(2, 0, 3, 12) - AIC:4427.734327424761
SARIMA(3, 1, 1)x(3, 0, 0, 12) - AIC:1563.9116233111158

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(3, 1, 1)x(3, 0, 1, 12) - AIC:1565.5617140741576

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(3, 1, 1)x(3, 0, 2, 12) - AIC:1566.5823059906488

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(3, 1, 1)x(3, 0, 3, 12) - AIC:2735.745046173969
SARIMA(3, 1, 2)x(0, 0, 0, 12) - AIC:2392.1921725797465

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(3, 1, 2)x(0, 0, 1, 12) - AIC:2085.217174356195

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(3, 1, 2)x(0, 0, 2, 12) - AIC:1849.3292775881957

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(3, 1, 2)x(0, 0, 3, 12) - AIC:1181.4956729014134
SARIMA(3, 1, 2)x(1, 0, 0, 12) - AIC:1918.9458282693686

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(3, 1, 2)x(1, 0, 1, 12) - AIC:1904.8144719564857

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(3, 1, 2)x(1, 0, 2, 12) - AIC:1736.0016351448617

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(3, 1, 2)x(1, 0, 3, 12) - AIC:2256.8074479242377
SARIMA(3, 1, 2)x(2, 0, 0, 12) - AIC:1738.320379909521

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(3, 1, 2)x(2, 0, 1, 12) - AIC:1737.2517825868829

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(3, 1, 2)x(2, 0, 2, 12) - AIC:1736.930169588231

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(3, 1, 2)x(2, 0, 3, 12) - AIC:2315.05127794128
SARIMA(3, 1, 2)x(3, 0, 0, 12) - AIC:1563.137870783618

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(3, 1, 2)x(3, 0, 1, 12) - AIC:1565.0292067337928

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(3, 1, 2)x(3, 0, 2, 12) - AIC:1566.7896372438552

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(3, 1, 2)x(3, 0, 3, 12) - AIC:2317.0750458271464

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(3, 1, 3)x(0, 0, 0, 12) - AIC:2354.174527275618

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(3, 1, 3)x(0, 0, 1, 12) - AIC:2070.5911262733666

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(3, 1, 3)x(0, 0, 2, 12) - AIC:1835.0340101951513

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(3, 1, 3)x(0, 0, 3, 12) - AIC:7511.6310689813445

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(3, 1, 3)x(1, 0, 0, 12) - AIC:1920.8088506948184

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(3, 1, 3)x(1, 0, 1, 12) - AIC:1892.7715860422315

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(3, 1, 3)x(1, 0, 2, 12) - AIC:1722.2106722042117

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(3, 1, 3)x(1, 0, 3, 12) - AIC:3343.161719416555

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(3, 1, 3)x(2, 0, 0, 12) - AIC:1739.9796307502525

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(3, 1, 3)x(2, 0, 1, 12) - AIC:1737.8460282756726

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(3, 1, 3)x(2, 0, 2, 12) - AIC:1724.2062260580828

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(3, 1, 3)x(2, 0, 3, 12) - AIC:3083.9484080995217
SARIMA(3, 1, 3)x(3, 0, 0, 12) - AIC:1565.0970880236575

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(3, 1, 3)x(3, 0, 1, 12) - AIC:1567.0070121749645

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(3, 1, 3)x(3, 0, 2, 12) - AIC:1571.2183145544595

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA(3, 1, 3)x(3, 0, 3, 12) - AIC:3408.767325414378

C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1335644396.py:13: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
  SARIMA_AIC = SARIMA_AIC.append({'param':param,'seasonal':param_seasonal ,'AIC': results_SARIMA.aic}, ignore_index=True)

SARIMA_AIC.sort_values(by='AIC',ascending=True)

	param	seasonal	AIC
147	(2, 1, 1)	(0, 0, 3, 12)	14.000000
163	(2, 1, 2)	(0, 0, 3, 12)	16.000000
179	(2, 1, 3)	(0, 0, 3, 12)	18.000000
227	(3, 1, 2)	(0, 0, 3, 12)	1181.495673
236	(3, 1, 2)	(3, 0, 0, 12)	1563.137871
...	...	...	...
19	(0, 1, 1)	(0, 0, 3, 12)	7666.628947
195	(3, 1, 0)	(0, 0, 3, 12)	7736.379197
3	(0, 1, 0)	(0, 0, 3, 12)	7736.558287
131	(2, 1, 0)	(0, 0, 3, 12)	7736.941952
67	(1, 1, 0)	(0, 0, 3, 12)	7740.669178

256 rows × 3 columns

• The combination(𝑝,𝑑,𝑞)(2,1,1) and seasonal (P,D,Q,s)=(0,0,3,12) has the lowest AIC (14.000). This suggests that this model fits the data best among the tested combinations.

import statsmodels.api as sm

auto_SARIMA = sm.tsa.statespace.SARIMAX(df_train['Sparkling'],order=(3,1,2),seasonal_order=(1,0,3,12),enforce_stationarity=False,
                                       enforce_invertibility=False)
results_auto_SARIMA = auto_SARIMA.fit(maxiter=1000)
print(results_auto_SARIMA.summary())

C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)
C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\tsa\base\tsa_model.py:473: ValueWarning: No frequency information was provided, so inferred frequency M will be used.
  self._init_dates(dates, freq)

                                         SARIMAX Results                                          
==================================================================================================
Dep. Variable:                                  Sparkling   No. Observations:                  144
Model:             SARIMAX(3, 1, 2)x(1, 0, [1, 2, 3], 12)   Log Likelihood               -1118.404
Date:                                    Sun, 19 Jan 2025   AIC                           2256.807
Time:                                            11:44:23   BIC                           2283.251
Sample:                                        01-31-1980   HQIC                          2267.521
                                             - 12-31-1991                                         
Covariance Type:                                      opg                                         
==============================================================================
                 coef    std err          z      P>|z|      [0.025      0.975]
------------------------------------------------------------------------------
ar.L1         -1.0985     11.158     -0.098      0.922     -22.967      20.770
ar.L2          0.2057    624.195      0.000      1.000   -1223.195    1223.606
ar.L3         -0.2747    214.364     -0.001      0.999    -420.420     419.870
ma.L1          0.8415      0.234      3.596      0.000       0.383       1.300
ma.L2         -0.7274      0.004   -196.158      0.000      -0.735      -0.720
ar.S.L12       0.9089      7.202      0.126      0.900     -13.207      15.024
ma.S.L12   -4.717e+12   1.27e-07  -3.72e+19      0.000   -4.72e+12   -4.72e+12
ma.S.L24    4.664e+12   1.22e-10   3.83e+22      0.000    4.66e+12    4.66e+12
ma.S.L36   -4.342e+12   6.13e-12  -7.09e+23      0.000   -4.34e+12   -4.34e+12
sigma2      1.779e+06      0.183    9.7e+06      0.000    1.78e+06    1.78e+06
===================================================================================
Ljung-Box (L1) (Q):                   9.16   Jarque-Bera (JB):              5691.40
Prob(Q):                              0.00   Prob(JB):                         0.00
Heteroskedasticity (H):               0.00   Skew:                             5.23
Prob(H) (two-sided):                  0.00   Kurtosis:                        37.70
===================================================================================

Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).
[2] Covariance matrix is singular or near-singular, with condition number 1.13e+44. Standard errors may be unstable.

C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\base\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "

• The current SARIMAX model has issues with parameter significance, residual behavior, and overfitting of seasonal terms. Refining the model or exploring alternative methods should improve its performance.

results_auto_SARIMA.plot_diagnostics();

• The presence of autocorrelation and patterns in the residuals suggests that the model is inadequate in capturing all data patterns.
• The residuals deviate significantly from normality (as seen in the histogram and Q-Q plot). This could lead to unreliable predictions and invalid statistical tests.

#Predict on the Test Set using this model and evaluate the model.
predicted_auto_SARIMA = results_auto_SARIMA.get_forecast(steps=len(df_test))

predicted_auto_SARIMA.summary_frame(alpha=0.05).head()

Sparkling	mean	mean_se	mean_ci_lower	mean_ci_upper
1992-01-31	-2.352961e+22	NaN	NaN	NaN
1992-02-29	3.268265e+22	NaN	NaN	NaN
1992-03-31	-4.539623e+22	NaN	NaN	NaN
1992-04-30	6.305540e+22	NaN	NaN	NaN
1992-05-31	-8.758400e+22	NaN	NaN	NaN

• The absence of standard errors (NaN) indicates that the model failed to calculate reliable uncertainty bounds around the forecasts. This is likely due to numerical issues during optimization.
• The forecasted mean values alternate between large positive and negative numbers, which are unrealistic and suggest that the model has diverged.
• These values are not meaningful and cannot be used for practical decision-making.

rmse = mean_squared_error(df_test['Sparkling'],predicted_auto_SARIMA.predicted_mean,squared=False)
mape = mean_absolute_percentage_error(df_test['Sparkling'],predicted_auto_SARIMA.predicted_mean)
print('RMSE:',rmse,'\nMAPE:',mape)

RMSE: 5.093366358404006e+27 
MAPE: 1.0559610841399717e+26

• The extremely large values for RMSE (5.09×10^27) and MAPE (1.06×10^26) indicate that your SARIMA model's test performance is severely poor.

temp_resultsDf = pd.DataFrame({'RMSE': rmse,'MAPE':mape}
                           ,index=['SARIMA(2,1,3)(1,0,3,12)'])


resultsDf = pd.concat([resultsDf,temp_resultsDf])

resultsDf

	RMSE	MAPE
ARIMA(2,1,2)	1.309632e+03	4.210620e+01
SARIMA(2,1,3)(1,0,3,12)	5.093366e+27	1.055961e+26

resultsDf2 = pd.concat([resultsDf,temp_resultsDf])
resultsDf2

	RMSE	MAPE
ARIMA(2,1,2)	1.309632e+03	4.210620e+01
SARIMA(2,1,3)(1,0,3,12)	5.093366e+27	1.055961e+26
SARIMA(2,1,3)(1,0,3,12)	5.093366e+27	1.055961e+26

resultsDf3 = resultsDf2[['RMSE']]
resultsDf3.rename(columns = { 'RMSE' : 'Test RMSE'}, inplace = True)
resultsDf3

C:\Users\HUAWEI\AppData\Local\Temp\ipykernel_17780\1466318677.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  resultsDf3.rename(columns = { 'RMSE' : 'Test RMSE'}, inplace = True)

	Test RMSE
ARIMA(2,1,2)	1.309632e+03
SARIMA(2,1,3)(1,0,3,12)	5.093366e+27
SARIMA(2,1,3)(1,0,3,12)	5.093366e+27

Model Comparison and Final Model Selection

comparison_model_table = pd.concat([resultsDf1, resultsDf3])
comparison_model_table.sort_values(by = 'Test RMSE')

	Test RMSE
Alpha= 0.3,Beta=0.3,Gamma=0.3,TripleExponentialSmoothing	4.126138e+02
Alpha= 0.5,Beta=0.3,Gamma=0.3,TripleExponentialSmoothing	4.126138e+02
SimpleAverageModel	1.268683e+03
SimpleExponentialSmoothing	1.296358e+03
ARIMA(2,1,2)	1.309632e+03
RegressionOnTime	1.337090e+03
SimpleExponentialSmoothing(α=0.3)	1.900059e+03
DoubleExponentialSmoothing(α=0.688)	1.923793e+03
NaiveModel	3.979915e+03
Alpha=0.6,Beta=0.00010,DoubleExponentialSmoothing	1.381440e+04
Alpha=0.6,Beta=0.3,DoubleExponentialSmoothing	1.381440e+04
SARIMA(2,1,3)(1,0,3,12)	5.093366e+27
SARIMA(2,1,3)(1,0,3,12)	5.093366e+27

• Best model among all models is Triple Exponential Smoothing models with parameters:α=0.3,β=0.3,γ=0.3, and α=0.5,β=0.3,γ=0.3 have the lowest RMSE of 412.6138, making them the best-performing models for this dataset.
• SARIMA(2,1,3)(1,0,3,12) model is very Poor-Performing Models

Forecasting using Final Model

Based on the model-building exercise, let's build the most optimum model(s) on the complete data and predict 12 months into the future with appropriate confidence intervals/bands

model = ExponentialSmoothing(df['Sparkling'],trend='additive',seasonal='multiplicative',freq='M')
# fit model
model_fit = model.fit(smoothing_level=0.3,smoothing_trend=0.3,smoothing_seasonal=0.3,optimized=False,use_brute=True)
# make prediction
yhat = model_fit.predict(start='31-08-1995',end='31-08-1996')

C:\Users\HUAWEI\anaconda3\Lib\site-packages\statsmodels\tsa\base\tsa_model.py:154: UserWarning: Parsing dates in DD/MM/YYYY format when dayfirst=False (the default) was specified. This may lead to inconsistently parsed dates! Specify a format to ensure consistent parsing.
  date_key = Timestamp(key)

plt.figure(figsize=(18,9))
plt.plot(df['Sparkling'], label='Actual Dataset')


plt.plot(yhat,label='Alpha = Beta = Gamma = 0.3, Triple Exponential Smoothing Model')


plt.legend(loc='best')
plt.grid();

• In the below code, we have calculated the upper and lower confidence bands at 95% confidence level
• Here we are taking the multiplier to be 1.96 as we want to plot with respect to a 95% confidence intervals.

pred_1_df = pd.DataFrame({'lower_CI':yhat - 1.96*np.std(model_fit.resid,ddof=1),
                          'prediction':yhat,
                          'upper_ci':yhat + 1.96*np.std(model_fit.resid,ddof=1)})
pred_1_df.head()

	lower_CI	prediction	upper_ci
1995-08-31	1014.069331	1854.686308	2695.303286
1995-09-30	1650.369143	2490.986121	3331.603098
1995-10-31	2498.588142	3339.205119	4179.822096
1995-11-30	3415.513796	4256.130773	5096.747751
1995-12-31	6048.795192	6889.412170	7730.029147

pred_1_df.tail()

	lower_CI	prediction	upper_ci
1996-04-30	1573.333217	2413.950194	3254.567172
1996-05-31	1335.714857	2176.331834	3016.948811
1996-06-30	1212.044951	2052.661928	2893.278906
1996-07-31	1597.967939	2438.584916	3279.201894
1996-08-31	1522.187178	2362.804155	3203.421132

# plot the forecast along with the confidence band

axis = df.plot(label='Actual', figsize=(15,8))
pred_1_df['prediction'].plot(ax=axis, label='Forecast', alpha=0.5)
axis.fill_between(pred_1_df.index, pred_1_df['lower_CI'], pred_1_df['upper_ci'], color='k', alpha=.15)
axis.set_xlabel('Year-Months')
axis.set_ylabel('Sales')
plt.legend(loc='best')
plt.grid()
plt.show()

• The forecast closely follows the seasonal pattern observed in the historical data.
• The predicted sales increase over time, aligning with the upward trend in the historical data.
• The confidence interval widens as the forecast moves further into the future, reflecting increased uncertainty in predictions for longer time horizons.
• The upward trend in the forecast matches the historical trend, suggesting the model accurately incorporates the long-term growth in sales.
• The forecast appears to extend smoothly from the historical data, suggesting a good fit of the model to the historical patterns.

Business Insights and Recommendations

• Trend Analysis: The forecasted values are increasing over time, suggesting a strong upward trend in the data.

• Uncertainty in Forecasts:The confidence intervals (lower and upper CI) widen as we move further into the future, reflecting increased uncertainty in predictions over longer time horizons.

• Seasonal Component:The predictions show a consistent seasonal influence, as the model adjusts based on recurring patterns in the data (e.g., periodic highs and lows).

• The data shows clear seasonal patterns with spikes occurring at regular intervals, likely representing periodic increases in sales (e.g., due to holidays, seasonal demand, etc.).

• There is also a noticeable trend in the data, with the sales level increasing over the years.

• ABC Estate Wines should be prepared depending on this forecast to anticipate high and low sales periods and plan inventory, marketing, and staffing accordingly.

• The forecast indicates continued growth in sales.which will support strategies to scale operations.

• ABC Estate Wines should Ensure adequate stock during high-demand periods (e.g., late Q4 and early Q1 based on the peaks observed).

• Plan promotions and production around these periods to maximize revenue without overstocking.

• ABC Estate Wines should Promote other wine types during slower seasons to balance overall sales (e.g., offering discounts or special bundles).

• The increasing trend in sales suggests growing demand. ABC Estate Wines can explore new markets or customer segments to sustain growth.

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